从所有可能的序列的稀疏采样中预测单克隆抗体结合序列
Pritha Bisarad1,2,3,4, Laimonas Kelbauskas5,6, Akanksha Singh1,2,7
1School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
Communications biology
|August 12, 2024
概括
在稀疏序数据上训练的机器学习模型可以准确预测抗体结合. 这种方法有效地识别出用于治疗和诊断应用的特定抗体序列.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 免疫学 免疫学 免疫学
背景情况:
- 机器学习模型可以预测目标蛋白与序列的结合.
- 高精度可以通过稀疏,不偏见的序样本采样来实现.
研究的目的:
- 使用抗体-结合数据探索高度序列特异性的分子识别.
- 评估在抗体结合的稀疏随机序列数据上训练的网络模型的预测能力.
主要方法:
- 测量了8个单克隆抗体 (mAbs) 与121,715个近随机序列的结合.
- 在序列绑定值上训练网络模型,以预测绑定亲和力.
- 在同源和in silico生成的随机序列上评估模型性能.
主要成果:
- 训练有素的模型始终将同源序列列在所有mAbs.的前100名中.
- 在8个mAbs中的6个中,同源序列排在前10位.
- 在预测特定分子识别方面表现出高准确性.
结论:
- 对序列的稀疏随机采样对于全面的预测模型来说是足够的.
- 这种方法显示出选择特定单克隆抗体的潜力,用于治疗和诊断.
- 验证了机器学习用于预测高度特定的分子识别的使用.
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